Abstract
This retrospective cross-sectional study aimed to evaluate the predictive value of SII (Systemic Immune Inflammation Index) and PNI (Prognostic Nutritional Index) with blood ratios on mortality in diabetic foot patients who underwent below-knee amputation. A total of 231 living (n = 71; 30.7%) and exitus (n = 160; 69.3%) patients were evaluated. The mortality group was divided into 3 groups: 30-day mortality (n = 62; 38.8%), 1-year mortality (n = 62; 38.8%), and over-1-year mortality (n = 36; 22.5%). The hemogram, SII, and PNI parameters of the patients were evaluated. Age, some blood count parameters and SII were significantly higher in the exitus group (P < .05). The lymphocyte, monocyte, eosinophil, albumin, and PNI levels were significantly higher in the living group (P < .05). Mortality was significantly predicted by age (B [regression coefficient] = 0.026, P < .05), NLR (neutrophil lymphocyte ratio) (B = −0.065, P < .05), PNI (B = −0.100, P < .01), and SII (B = 0.00000024, P < .01). The predictive values of CAR (C reactive protein albumin ratio), PNI, and SII were 77.3%, 77.0%, and 76.1%, respectively. For CAR of 30.88 cutoff value, the sensitivity and specificity were 79.4% and 64.8%, respectively. For the PNI 22.0143 cutoff value, the sensitivity and specificity were 66.9% and 5.6%, respectively. For the SII 732249.2481 cutoff value, the sensitivity and specificity were 91.9% and 31.0%, respectively. The predictive value of the PNI was significant for mortality time (B = 0.058; P < .01). The predictive value of PNI for 30-day mortality was significant (AUC (area under curve):0.632; P < .01), whereas its predictive value for 1-year mortality and over-1-year mortality after below-knee amputation was statistically insignificant (P > .05). Both the SII and PNI may be evaluated and used to predict mortality after below-knee amputation. The SII had a significant predictive value for 30-day mortality after below-knee amputation.
Keywords: below knee amputation, mortality, prognostic nutritional index, systemic immune inflammation index
1. Introduction
Below-knee amputation is one of the most important health problems, both in terms of individuals’ quality of life and public health, and in the field of orthopedics.[1–3] Below-knee amputations, like other operations, cause many complications,[4–6] postoperative pain[7] and mortality. Skew, sagittal, medial, and fish-mouth flaps are some of the different amputation procedures for below-knee amputation, and researches showed that there were no appreciable differences between the various amputation techniques in the rates of primary stump healing, postoperative surgical site necrosis, or mortality.[1] It is also difficult to determine which amputation method results in more complications and mortality in below-knee amputation since the differences between the basic amputation methods cannot be clearly demonstrated. However, the common point for all methods, especially the 30-month mortality and 1-year mortality, is an important health problem in general mortality rates.
Diabetic foot is one of the most common and life-quality-reducing complications of diabetes mellidus.[8] Its prevalence varies from 6.6% in high-income countries to 12.3% in low-income countries.[9] Because of poor wound healing, lower extremity rates are higher in diabetic feet, as are hospitalization rates.[10]
The Systemic Immune Inflammation Index (SII) is an immune inflammatory indicator defined as the neutrophil product of the platelet/lymphocyte ratio. The SII is known to have prognostic value in hepatocellular carcinoma.[11] First, Hu et al[12] introduced SII as a third inflammation marker that combines neutrophil/lymphocyte (NLR) and platelet/lymphocyte (PLR) ratios as a single indicator.[11–14] Although NLR and PLR are indicators that are widely used in mortality and disease prognosis, especially in cancer, the SII evaluates the 2 indicators together, allowing the indicators to be evaluated with higher performance.
In contrast to other mortality and progression ratios, the Prognostic Nutritional Index (PNI) is used to predict postoperative complications in cancer patients. The PNI provides the nutritional status of patients based on serum albumin level and total lymphocyte count.[15] The PNI was first introduced in 1980 and is used for cardiovascular diseases and other cancer types.[16] It is also argued to be an indicator of the quality of life of patients.[17] Nutrition is an important treatment and quality of life indicator in below-knee amputation, cancer, and other cardiovascular diseases. In this respect, PNI may have prognostic value, especially for mortality in below-knee amputations.
Mortality after below-knee amputation has been investigated in several clinical studies. However, few studies have analyzed the predictive value of the SII and PNI indicators for mortality after below-knee amputation. In this retrospective cross-sectional study, we aimed to evaluate the predictive value of the SII and PNI with blood ratios on mortality after low mortality in patients with below-knee amputation.
2. Methods
This study was conducted using a descriptive cross-sectional method with a retrospective design. A total of 231 patient files with below-knee operations for diabetic foot were evaluated and divided into 2 sub-groups: living (n = 71; 30.7%) and exitus (n = 160; 69.3%) groups between 2016–2022. The mortality group was divided into 3 groups: 30-day mortality (n = 62; 38.8%), 1-year mortality (n = 62; 38.8%), and over-1-year mortality (n = 36; 22.5%). The hemogram, SII, and PNI parameters of the patients were evaluated.
The inclusion and exclusion criteria are as follows:
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Not having a history of chronic drug use may affect the indicators in the study.
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Having at least 12 month follow up for diabetic foot.
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The completeness of the research data in the files.
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Adequate access to information on mortality and survival.
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Consistency and appropriateness of data in patient files.
Ethics committee approval was obtained from Kutahya Health Sciences University Presidency of Non-Interventional Clinical Research Ethics Committee (grant number E-41997688-050.99-98814).
2.1. Statistical methods
Nominal and ordinal parameter descriptions were performed using frequency analysis, whereas scale parameters were described using means and standard deviations. Fisher Exact test was used for nominal difference analysis. The Kolmogorov-Smirnov test was used to assess the normality of the scale parameters. The Mann–Whitney U test was used for nonparametric differences between the 2 groups. Spearman rho correlation analysis was used to determine the relationship between mortality and research parameters. Because all regression techniques include linearization deviations at a certain level,[18] logistic methods were used. Cox regression analysis was used for multivariate analysis of mortality-related parameters, and a Generalized Linear Model (Logit) was used for mortality time-related parameters. Receiver operating characteristic (ROC) analysis was used to determine the predictive value of the research parameters. SPSS 17.0 for windows was used for analysis at a 95% Confidence Interval and a significance level of 0.05.
3. Results
Age, neutrophil count, PLT (platelet), MPV (mean platelet volume), C-reactive protein (CRP), NLR (neutrophil lymphocyte ratio), CAR (CRP albumin ratio), MPVLR (mean platelet volume lymphocyte ratio), MPVPR (mean platelet volume platelet ratio), and SII were significantly higher in the exitus group (P < .05). The lymphocyte, monocyte, eosinophil, albumin, and PNI levels were significantly higher in the living group (P < .05). Sex, hemoglobin, and monocyte/lymphocyte ratio differences were insignificant between the mortality groups (P > .05) (Table 1).
Table 1.
Sex, age, and clinical parameters with indicators for patient groups and difference analysis results.
| Mortality | P value | ||
|---|---|---|---|
| No (n = 71; 30.7%) | Yes (160; 69.3%) | ||
| Sex, n (%) | .054* | ||
| Female | 22 (31.0) | 69 (43.1) | |
| Male | 49 (69.0) | 91 (56.9) | |
| Age | 68.49 ± 9.70 | 74.31 ± 10.03 | .000** |
| Neutrophile | 7.86 ± 3.59 | 11.56 ± 5.77 | .000** |
| Lymphocyte | 1.68 ± 0.73 | 1.34 ± 0.60 | .001** |
| Monocyte | 0.69 ± 0.25 | 0.59 ± 0.32 | .006** |
| Eisonophile | 0.20 ± 0.18 | 0.17 ± 0.44 | .000** |
| PLT | 251.07 ± 87.37 | 311.24 ± 120.37 | .000** |
| MPV | 8.79 ± 1.10 | 9.33 ± 1.43 | .004** |
| Albumin | 2.83 ± 0.36 | 2.43 ± 0.42 | .000** |
| Hemoglobin | 12.05 ± 10.93 | 10.42 ± 1.59 | .058** |
| CRP | 78.47 ± 49.59 | 148.36 ± 99.65 | .000** |
| NLR | 5.83 ± 4.77 | 11.56 ± 10.43 | .000** |
| MLR | 0.51 ± 0.31 | 0.57 ± 0.52 | .826** |
| CAR | 28.66 ± 19.75 | 64.50 ± 46.31 | .000** |
| MPVLR | 6.26 ± 2.81 | 8.92 ± 5.77 | .000** |
| MPVPR | 0.04 ± 0.01 | 0.04 ± 0.02 | .000** |
| PNI | 28.32 ± 3.57 | 24.27 ± 4.21 | .000** |
| SII | 1478127.21 ± 1304853.78 | 3400115.19 ± 3080553.20 | .000** |
| 30-d mortality, n (%) | - | 38.8 | N/A |
| 1-yr mortality, n (%) | - | 38.8 | N/A |
| Over-1-yr mortality, n (%) | - | 22.5 | N/A |
Bold values indicate statistically significant parameters.
CAR = C reactive protein albumin ratio, CRP = C-reactive protein, MLR = monocyte lymphocyte ratio, MPV = mean platelet volume, MPVLR = mean platelet volume lymphocyte ratio, MPVPR = mean platelet volume platelet ratio, NLR = neutrophil lymphocyte ratio, PLT = platelet, PNI = Prognostic Nutritional Index, SII = Systemic Immune Inflammation Index.
Fisher Exact Test.
Mann–Whitney U test, N/A: Not Applicable.
Mortality after below-knee amputation and age (R = 0.262; P < .01), neutrophile (R = 0.345; P < .01), lymphocyte (r = −0.213; P < .01), monocyte (r = −0.183; P < .01), eosinophile (r = −0.318; P < .01), PLT (R = 0.257; P < .01), MPV (R = 0.189; P < .01), albumin (r = −0.428; P < .01), CRP (R = 0.391; P < .01), NLR (R = 0.366; P < .01), CAR (R = 0.436; P < .01), MPVLR (R = 0.260; P < .01), MPVPR (r = −0.216; P < .01), PNI (r = −0.432; P < .01) and SII (R = 0.417; P < .01) were statistically significant (Table 2).
Table 2.
Spearman rho correlation between mortality and significantly different research parameters.
| Mortality | r | P |
|---|---|---|
| Age | 0.262* | .000 |
| Neutrophile | 0.345* | .000 |
| Lymphocyte | −0.213* | .001 |
| Monocyte | −0.183* | .005 |
| Eisonophile | −0.318* | .000 |
| PLT | 0.257* | .000 |
| MPV | 0.189* | .004 |
| Albumin | −0.428* | .000 |
| Hemoglobin | −0.125 | .057 |
| CRP | 0.391* | .000 |
| NLR | 0.366* | .000 |
| MLR | 0.014 | .827 |
| CAR | 0.436* | .000 |
| MPVLR | 0.260* | .000 |
| MPVPR | −0.216* | .001 |
| PNI | −0.432* | .000 |
| SII | 0.417* | .000 |
CAR = C reactive protein albumin ratio, CRP = C-reactive protein, MLR = monocyte lymphocyte ratio, MPV = mean platelet volume, MPVLR = mean platelet volume lymphocyte ratio, MPVPR = mean platelet volume platelet ratio, NLR = neutrophil lymphocyte ratio, PLT = platelet, PNI = Prognostic Nutritional Index, SII = Systemic Immune Inflammation Index.
P < .01.
To evaluate the predictive value of indicators of mortality at the multivariate level, binary logistic regression analysis was used. Since indicators are ratios of blood parameters, only indicators and parameters that were not included in the ratios were included in the regression model to prevent cointegration. Cox regression analysis showed that mortality was significantly predicted by age (B [regression coefficient] = 0.026, P < .05), NLR (B = −0.065, P < .05), PNI (B = −0.100, P < .01), and SII (B = 0.00000024, P < .01) (Table 3).
Table 3.
Cox regression analysis for mortality and related parameters.
| B | S.E. | Wald | P | OR | 95% C.I.for OR | ||
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| Age | 0.026 | 0.008 | 10.056 | .002 | 1.027 | 1.010 | 1.043 |
| NLR | −0.065 | 0.024 | 7.244 | .007 | 0.937 | 0.894 | 0.982 |
| CAR | 0.003 | 0.002 | 2.251 | .134 | 1.003 | 0.999 | 1.007 |
| MPVLR | 0.035 | 0.023 | 2.355 | .125 | 1.035 | 0.990 | 1.083 |
| MPVPR | 6.489 | 5.313 | 1.492 | .222 | 657.843 | 0.020 | 21896836.934 |
| PNI | −0.100 | 0.023 | 19.400 | .000 | 0.905 | 0.865 | 0.946 |
| SII | 0.00000024 | 0.000 | 11.281 | .001 | 1.000 | 1.000 | 1.000 |
B = regression coefficient, CAR = C reactive protein albumin ratio, CI = Confidence Interval, MPVLR = mean platelet volume lymphocyte ratio, MPVPR = mean platelet volume platelet ratio, NLR = neutrophil lymphocyte ratio, OR = Odds Ratio, PNI = Prognostic Nutritional Index, S.E. = standard error, SII = Systemic Immune Inflammation Index.
The predictive values of CAR (AUC [area under curve]: 0.773; P < .01), PNI (AUC:0.770; P < .01), and SII (AUC: 0.761; P < .01) for mortality after below-knee amputation were significant. The predictive values of CAR, PNI, and SII were 77.3%, 77.0%, and 76.1%, respectively. For CAR of 30.88 cut off value, the sensitivity and specificity were 79.4% and 64.8%, respectively. For the PNI 22.0143 cutoff value, the sensitivity and specificity were 66.9% and 5.6%, respectively. For the PNI cutoff value of 32.0043, the sensitivity and specificity were 5.6% and 87.3%, respectively. For the SII 732249.2481 cutoff value, the sensitivity and specificity were 91.9% and 31.0%, respectively (Fig. 1).
Figure 1.
ROC (Receiver operating characteristic) Curve analysis for predictive value of CAR (C reactive protein albumin ratio), PNI (Prognostic Nutritional Index) and SII (Systemic Immune Inflammation Index) for mortality.
The mortality time after below-knee amputation was significantly correlated with neutrophil count (r = −0.208; P < .01), MPV (r = −0.173; P < .05), albumin level (R = 0.310; P < .01), hemoglobin level (R = 0.294; P < .01), CAR (r = −0.182; P < .05), and PNI (R = 0.308; P < .01) (Table 4) (Fig. 2).
Table 4.
Spearman rho correlation between mortality time and research parameters.
| Mortality time | r | P |
|---|---|---|
| Age | −0.146 | .066 |
| Neutrophile | −0.208** | .008 |
| Lymphocyte | 0.037 | .639 |
| Monocyte | 0.022 | .779 |
| Eisonophile | 0.006 | .937 |
| PLT | −0.114 | .152 |
| MPV | −0.173* | .029 |
| Albumin | 0.310** | .000 |
| Hemoglobin | 0.294** | .000 |
| CRP | −0.136 | .086 |
| NLR | −0.135 | .088 |
| MLR | −0.004 | .956 |
| CAR | −0.182* | .021 |
| MPVLR | −0.095 | .234 |
| MPVPR | 0.024 | .766 |
| PNI | 0.308** | .000 |
| SII | −0.142 | .073 |
CAR = C reactive protein albumin ratio, CRP = C-reactive protein, MLR = monocyte lymphocyte ratio, MPV = mean platelet volume, MPVLR = mean platelet volume lymphocyte ratio, MPVPR = mean platelet volume platelet ratio, NLR = neutrophil lymphocyte ratio, PLT = platelet, PNI = Prognostic Nutritional Index, SII = Systemic Immune Inflammation Index.
P < .05.
P < .01.
Figure 2.
Kaplan–Meier for mortality patients after below-knee operation.
The Generalized Linear Model (Logit) for mortality time and correlated parameter results showed that the predictive value for PNI was significant (B = 0.058; P < .01) (Table 5).
Table 5.
Generalized Linear Model (Logit) for mortality time and correlated parameters.
| Parameter | B | S.E. | 95% Wald confidence interval | Hypothesis test | |||
|---|---|---|---|---|---|---|---|
| Lower | Upper | Wald Chi-Square | df | P | |||
| (Intercept) | 0.415 | 0.4147 | −0.397 | 1.228 | 1.003 | 1 | .317 |
| CAR | 6.166E-5 | 0.0014 | −0.003 | 0.003 | 0.002 | 1 | .964 |
| PNI | 0.058 | 0.0151 | 0.029 | 0.088 | 15.045 | 1 | .000 |
| (Scale) | 0.527 | 0.0589 | 0.423 | 0.656 | |||
B = regression coefficient, CAR = C reactive protein albumin ratio, df = degree of freedom, PNI = prognostic nutritional index, S.E. = standard error.
The predictive value of PNI for 30-day mortality was significant (AUC:0.632; P < .01), whereas its predictive value for 1-year mortality and over-1-year mortality after below-knee amputation was statistically insignificant (P > .05). For the PNI of 20.005, the sensitivity and specificity were 77.4% and 15.3%, respectively. For the PNI 23.0058 value, sensitivity and specificity were 56.5% and 31.6%, respectively (Fig. 3).
Figure 3.
ROC (Receiver operating characteristic) Curve analysis for predictive value of PNI (Prognostic Nutritional Index) for 30-d mortality.
4. Discussion
In this study, the predictive value of blood parameters, especially SII and PNI, and their ratios on mortality in patients who underwent below-knee amputation were examined. In this context, first, the effect of indicators on mortality after below-knee surgery was investigated, and then the predictive power of indicators on 30-day, 1-year, and over-1-year mortality was analyzed at univariate and multivariate levels.
Below-knee amputations are an important public health and orthopedic health problem, with limited studies on mortality rates due to the lack of clear information about which amputation methods are superior.[1–3] Although there are important developments in the field of below-knee operations in terms of materials and methods, mortality after below-knee operations remains an important problem today.[19–23] The scarcity of studies on mortality and quality of life of patients after below-knee operations indicates that further clinical studies are needed on this subject. However, after the below-knee, changes occur not only in the daily lives of patients but also in their economic and social lives.
Unlike other indicators, the SII is not an indicator; it is an index that allows evaluation of the prognostic or predictive value of 2 different ratios together. NLR and PLR are among the most common mortality ratios; thus, it may be argued that SII increases the predictive value of NLR and PLR at the cross-product level.[24] Zhang et al[11] reported in their research on esophageal cancer that SII is an important prognostic factor for esophageal cancer and indicator for mortality. Hu et al[12] found that the SII is a significant indicator of the prognosis of hepatocellular carcinoma after curative resection. He et al[13] reported that SII is a significant outcome predictor of survival after radical surgery for gastric cancer. All these studies and literature reviews show that the SII can be an important indicator of postoperative mortality in diseases and operations where mortality is important. In our study, the SII had an important and high predictive value for postoperative mortality. Based on the SII measurement, it may be possible to monitor both the mortality probability and disease progression level after below-knee surgery. With further multicenter studies, it may be possible to obtain more information about the prognosis of patients.
The PNI is an indicator of nutrition for patients after surgery. For below-knee amputations, nutrition plays an important role in both the success of the treatment and quality of life quality.[15–17] In the research conducted In the literature, there has been no study related to PNI after below-knee amputation. However, cardiovascular and cancer studies have suggested its prognostic value for PNI. Yu et al[15] concluded in their research on radical cystectomy patients that preoperative PNI predicts postoperative pulmonary complications. In another study, Huang et al[16] stated that PNI had a 1-year mortality prediction value for patients with acute myocardial infarction. Wang et al[17] reported that the PNI, PLR, NLR, and monocyte lymphocyte ratio had prognostic predictive values in patients with cervical cancer. Yan et al[25] reported in their review that the PNI has a significant and independent prognostic value for many cancer types. In our study, the PNI had an important predictive value both in revealing the general postoperative mortality level and in estimating 30-day mortality. Although the PNI indicators of 1-year mortality and over-1-year mortality are not predictive, they are important in terms of 30-day mortality. PNI has an important value in terms of mortality in the first 30 days, since the follow-up is efficient in the first 30 days after the operation in our country, and there are patients who are not followed up afterwards. Many below-knee patients can make vital contributions to life by paying particular attention to this situation in clinical applications.
4.1. Limitations of the study
The most important limitation of this study is that it was single-center retrospective. In fact, although single-center and retrospective studies are important sources of data and information for clinical applications, the sample power is limited during the data collection phase, especially for patients who are missing or out of follow-up. In addition, in the long term, patients’ changing social environments and places of residence after amputation also result in exclusion from follow-up. Cross-comparisons can be made with more multicenter studies and further research can be conducted.
Another important limitation of this study is the small number of studies in this field. Although this makes the research pioneering and important, there was not enough study in the Discussion section to compare with related studies. It would be beneficial to encourage and support further research in this area.
4.2. Contribution to literature and importance of the study
The most important contribution of this study to the literature is that it pragmatically aims to both predict and reduce the mortality levels of patients after below-knee amputation and to predict the progression of the disease. In this regard, by looking at simple hemogram parameters such as SII and PNI, mortality can be determined after below-knee amputation using a calculation method without the need for additional analysis.
Another contribution of this study to the literature is that it is a pioneer in the field. The SII and PNI have been evaluated in many different diseases in terms of both prognostic value and postoperative mortality estimation in other cancer types. However, there are not enough studies on the prognosis and postoperative mortality after below-knee amputation. Therefore, this study provides an important basis for further studies and clinical applications.
5. Conclusion
Although the predictive value of SII and PNI indicators on mortality after below-knee amputation was statistically significant, only PNI had a significant predictive value in showing the time of mortality. This is an important finding in order to provide better quality health care to individuals by reducing 30-day mortality rates, which have become a serious public health problem, especially after below-knee amputation.
However, PNI does not have sufficient predictive power to estimate 1-year and over-1-year mortality rates. With larger samples, more multicenter studies, and multivariate analyses, it may be possible to predict 1-year mortality and over-1-year mortality levels and to provide more sensitive follow-up and care.
Acknowledgments
We thank Kadir Yilmaz for valuable statistical support.
Author contributions
Conceptualization: Selçuk Yilmaz, Mehmet Kurt, Turan Cihan Dülgeroğlu.
Data curation: Selçuk Yilmaz, Mehmet Kurt.
Formal analysis: Mehmet Kurt, Alaaddin Oktar Üzümcügil.
Funding acquisition: Selçuk Yilmaz, Mehmet Kurt, Turan Cihan Dülgeroğlu, Alaaddin Oktar Üzümcügil.
Investigation: Turan Cihan Dülgeroğlu, Alaaddin Oktar Üzümcügil.
Project administration: Selçuk Yilmaz.
Software: Alaaddin Oktar Üzümcügil.
Writing – original draft: Selçuk Yilmaz, Mehmet Kurt, Turan Cihan Dülgeroğlu.
Writing – review & editing: Selçuk Yilmaz, Mehmet Kurt, Turan Cihan Dülgeroğlu.
Abbreviations:
- AUC
- area under curve
- B
- regression coefficient
- CAR
- C reactive protein albumin ratio
- CRP
- C-reactive protein
- MPV
- mean platelet volume
- MPVLR
- mean platelet volume lymphocyte ratio
- MPVPR
- mean platelet volume platelet ratio
- NLR
- neutrophil lymphocyte ratio
- OR
- odds ratio
- PLR
- platelet lymphocyte ratio
- PLT
- platelet
- PNI
- prognostic nutritional index
- ROC
- receiver operating characteristic
- S.E.
- standard error
- SII
- systemic immune inflammation index
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
The authors have no funding and conflicts of interest to disclose..
How to cite this article: Yilmaz S, Kurt M, Dülgeroğlu TC, Üzümcügil AO. Predictive value of systemic immune inflammation index (SII) and prognostic nutritional index (PNI) on mortality after below-knee amputation. Medicine 2023;102:43(e35703).
Contributor Information
Mehmet Kurt, Email: dr_mehmet91@hotmail.com.
Turan Cihan Dülgeroğlu, Email: dr_turancihan@hotmail.com.
Alaaddin Oktar Üzümcügil, Email: alaaddinoktar.uzumcugil@ksbu.edu.tr.
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